RII Track-4:NSF: Spatiotemporal Modeling of Lithium-ion Battery Packs for Electric Vehicle Battery Management Systems

Recent reports of lithium-ion (Li-ion) battery overheating and catching fire in electric vehicles (EVs) have raised concerns about user safety and the broader acceptance of EVs. These incidents highlight the limitations of the onboard electronic system that monitors and controls the battery pack, referred to as the battery management system (BMS), in detecting such abnormal behavior.

Therefore, enhancing the BMS's capabilities to discern the battery's behavior becomes imperative to prevent catastrophic failures. A smart BMS capable of monitoring the smallest part of a battery pack in real-time and learning abnormal behavior for future prediction could be the key to addressing these safety concerns. Through this NSF EPSCoR RII Track-4 fellowship project, the PI will collaborate with experts at the Sandia National Laboratory (SNL) to develop a transformative solution for capturing and learning the dynamic behavior of Li-ion battery packs in EVs.

This innovative approach promises to enhance the BMS's predictive capabilities and drive health-centric decisions. Additionally, this initiative includes a comprehensive educational and outreach segment, aimed at promoting the participation of underrepresented students in research, integrating research findings into both graduate and undergraduate education, and facilitating K-12 outreach on Li-ion battery operation and safety through online video tutorials.

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows project will provide a fellowship to an Assistant Professor and training for a graduate student at the University of Alabama Huntsville. This work would be conducted in collaboration with researchers at the Sandia National Laboratory (SNL). The primary goals of the fellowship project are to develop: 1) an interconnected model of a Li-ion battery pack and 2) a deep neural network model to learn the spatial and temporal dynamics.

The project's intrinsic scientific merit revolves around comprehending the interplay between the electrical, thermal, and aging behavior of the Li-ion battery pack and how these intricately linked behaviors influence internal degradation propagation among cells, both spatially and temporally. Leveraging these insights, the project will, in Aim 1, conceive an interconnected electro-thermal-aging model for the battery pack.

A data-centric identification strategy will also be delineated to estimate the parameters of the interconnected model, drawing on graph theory and network inference. In Aim 2, a deep diffusion convolutional neural network (DD-CRNN) will be designed to learn the spatiotemporal dynamics of the pack. This physics-driven DD-CRNN model will be trained using a blend of experimental and synthetic data.

Relying on SNL's expansive pack-level testing infrastructure, the project will accumulate degradation and abuse data, which is essential for training the DD-CRNN and affirming the model's validity. The proposed model and learning framework are poised to transform battery health monitoring by delivering precise State of Charge (SOC), State of Health (SOH), and thermal parameter estimations.

This innovation will empower BMS with greater autonomy in decision-making by facilitating cell- and module-level health and anomaly detection. The project will chart a new frontier in power and energy management and critically minimize the risk of pack overheating and fire incidents, ensuring safer and more efficient battery utilization.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.